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Digital twins for cancer progression based on multiscale structured models and mechanistic learning

Objective

The personalization of oncology needs predictive methods to anticipate major pathological events and optimize clinical care for each patient. To address this critical demand, computational oncology has produced biomechanistic models for patient-specific cancer forecasting. Although these models are based on cancer growth (increase in extension), the main driver of potentially lethal disease and, hence, the basis for clinical decisions is cancer progression (increase in malignancy). However, despite known correlations between cancer progression and growth, the biophysical mechanisms that explain how cancer progression emerges and alters cancer growth remain unknown. DIGIPRO will address this crucial gap in knowledge. I will build a new class of biomechanistic models to describe the multiscale dynamics of cancer progression, enable its early detection at microscopic stage by using macroscopic clinical data, and construct clinically-practical digital twins to optimally adapt cancer monitoring for early detection of progression. To achieve this ambitious goal, I will focus on newly-diagnosed prostate cancer and (i) build novel micro and macroscale models jointly describing cancer growth and progression using a structured formulation over space, time, and malignancy dimensions; (ii) pioneer a multiscale structured model equipped with a mechanistic learning operator that uses macroscale model-based biomarkers of progression to reconstruct the microscale cancer malignancy profile; and (iii) combine adaptive and immersed isogeometric methods, reduced order models, and multi-objective risk-based optimization to construct practical digital twins of cancer progression and explore optimization of cancer monitoring. Thus, DIGIPRO will push the frontiers of cancer modeling, biology, and care, while also opening new horizons in mechanistic modeling of multiscale progressive phenomena in medicine (e.g. Alzheimer’s disease, atherosclerosis) and engineering (e.g. fracture, damage).

Fields of science (EuroSciVoc)

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This project's classification has been human-validated.

Programme(s)

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Topic(s)

Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.

Funding Scheme

Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.

HORIZON-ERC - HORIZON ERC Grants

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Call for proposal

Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.

(opens in new window) ERC-2025-STG

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Host institution

UNIVERSIDADE DA CORUNA
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 1 499 990,00
Address
CALLE DE LA MAESTRANZA 9
15001 La Coruna
Spain

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Region
Noroeste Galicia A Coruña
Activity type
Higher or Secondary Education Establishments
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Total cost

The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.

€ 1 499 990,00

Beneficiaries (1)

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